#
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# or more contributor license agreements.  See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership.  The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "License"); you may not use this file except in compliance
# with the License.  You may obtain a copy of the License at
#
#   http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an
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# KIND, either express or implied.  See the License for the
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# under the License.
from __future__ import annotations

import hashlib
import importlib
import importlib.machinery
import importlib.util
import os
import sys
import textwrap
import traceback
import zipfile
from datetime import datetime, timedelta
from pathlib import Path
from typing import TYPE_CHECKING, NamedTuple

from sqlalchemy import (
    Column,
    String,
)
from tabulate import tabulate

from airflow import settings
from airflow.configuration import conf
from airflow.exceptions import (
    AirflowClusterPolicyError,
    AirflowClusterPolicySkipDag,
    AirflowClusterPolicyViolation,
    AirflowDagCycleException,
    AirflowDagDuplicatedIdException,
    AirflowException,
)
from airflow.listeners.listener import get_listener_manager
from airflow.models.base import Base
from airflow.stats import Stats
from airflow.utils import timezone
from airflow.utils.dag_cycle_tester import check_cycle
from airflow.utils.docs import get_docs_url
from airflow.utils.file import (
    correct_maybe_zipped,
    get_unique_dag_module_name,
    list_py_file_paths,
    might_contain_dag,
)
from airflow.utils.log.logging_mixin import LoggingMixin
from airflow.utils.session import NEW_SESSION, provide_session
from airflow.utils.timeout import timeout
from airflow.utils.types import NOTSET
from airflow.utils.warnings import capture_with_reraise

if TYPE_CHECKING:
    from sqlalchemy.orm import Session

    from airflow.models.dag import DAG
    from airflow.models.dagwarning import DagWarning
    from airflow.utils.types import ArgNotSet


class FileLoadStat(NamedTuple):
    """
    Information about single file.

    :param file: Loaded file.
    :param duration: Time spent on process file.
    :param dag_num: Total number of DAGs loaded in this file.
    :param task_num: Total number of Tasks loaded in this file.
    :param dags: DAGs names loaded in this file.
    :param warning_num: Total number of warnings captured from processing this file.
    """

    file: str
    duration: timedelta
    dag_num: int
    task_num: int
    dags: str
    warning_num: int


class DagBag(LoggingMixin):
    """
    A dagbag is a collection of dags, parsed out of a folder tree and has high level configuration settings.

    Some possible setting are database to use as a backend and what executor
    to use to fire off tasks. This makes it easier to run distinct environments
    for say production and development, tests, or for different teams or security
    profiles. What would have been system level settings are now dagbag level so
    that one system can run multiple, independent settings sets.

    :param dag_folder: the folder to scan to find DAGs
    :param include_examples: whether to include the examples that ship
        with airflow or not
    :param safe_mode: when ``False``, scans all python modules for dags.
        When ``True`` uses heuristics (files containing ``DAG`` and ``airflow`` strings)
        to filter python modules to scan for dags.
    :param read_dags_from_db: Read DAGs from DB if ``True`` is passed.
        If ``False`` DAGs are read from python files.
    :param load_op_links: Should the extra operator link be loaded via plugins when
        de-serializing the DAG? This flag is set to False in Scheduler so that Extra Operator links
        are not loaded to not run User code in Scheduler.
    :param collect_dags: when True, collects dags during class initialization.
    :param known_pools: If not none, then generate warnings if a Task attempts to use an unknown pool.
    """

    def __init__(
        self,
        dag_folder: str | Path | None = None,
        include_examples: bool | ArgNotSet = NOTSET,
        safe_mode: bool | ArgNotSet = NOTSET,
        read_dags_from_db: bool = False,
        load_op_links: bool = True,
        collect_dags: bool = True,
        known_pools: set[str] | None = None,
    ):
        super().__init__()

        include_examples = (
            include_examples
            if isinstance(include_examples, bool)
            else conf.getboolean("core", "LOAD_EXAMPLES")
        )
        safe_mode = (
            safe_mode if isinstance(safe_mode, bool) else conf.getboolean("core", "DAG_DISCOVERY_SAFE_MODE")
        )

        dag_folder = dag_folder or settings.DAGS_FOLDER
        self.dag_folder = dag_folder
        self.dags: dict[str, DAG] = {}
        # the file's last modified timestamp when we last read it
        self.file_last_changed: dict[str, datetime] = {}
        self.import_errors: dict[str, str] = {}
        self.captured_warnings: dict[str, tuple[str, ...]] = {}
        self.has_logged = False
        self.read_dags_from_db = read_dags_from_db
        # Only used by read_dags_from_db=True
        self.dags_last_fetched: dict[str, datetime] = {}
        # Only used by SchedulerJob to compare the dag_hash to identify change in DAGs
        self.dags_hash: dict[str, str] = {}

        self.known_pools = known_pools

        self.dagbag_import_error_tracebacks = conf.getboolean("core", "dagbag_import_error_tracebacks")
        self.dagbag_import_error_traceback_depth = conf.getint("core", "dagbag_import_error_traceback_depth")
        if collect_dags:
            self.collect_dags(
                dag_folder=dag_folder,
                include_examples=include_examples,
                safe_mode=safe_mode,
            )
        # Should the extra operator link be loaded via plugins?
        # This flag is set to False in Scheduler so that Extra Operator links are not loaded
        self.load_op_links = load_op_links

    def size(self) -> int:
        """:return: the amount of dags contained in this dagbag"""
        return len(self.dags)

    @property
    def dag_ids(self) -> list[str]:
        """
        Get DAG ids.

        :return: a list of DAG IDs in this bag
        """
        return list(self.dags)

    @provide_session
    def get_dag(self, dag_id, session: Session = None):
        """
        Get the DAG out of the dictionary, and refreshes it if expired.

        :param dag_id: DAG ID
        """
        # Avoid circular import
        from airflow.models.dag import DagModel

        if self.read_dags_from_db:
            # Import here so that serialized dag is only imported when serialization is enabled
            from airflow.models.serialized_dag import SerializedDagModel

            if dag_id not in self.dags:
                # Load from DB if not (yet) in the bag
                self._add_dag_from_db(dag_id=dag_id, session=session)
                return self.dags.get(dag_id)

            # If DAG is in the DagBag, check the following
            # 1. if time has come to check if DAG is updated (controlled by min_serialized_dag_fetch_secs)
            # 2. check the last_updated and hash columns in SerializedDag table to see if
            # Serialized DAG is updated
            # 3. if (2) is yes, fetch the Serialized DAG.
            # 4. if (2) returns None (i.e. Serialized DAG is deleted), remove dag from dagbag
            # if it exists and return None.
            min_serialized_dag_fetch_secs = timedelta(seconds=settings.MIN_SERIALIZED_DAG_FETCH_INTERVAL)
            if (
                dag_id in self.dags_last_fetched
                and timezone.utcnow() > self.dags_last_fetched[dag_id] + min_serialized_dag_fetch_secs
            ):
                sd_latest_version_and_updated_datetime = (
                    SerializedDagModel.get_latest_version_hash_and_updated_datetime(
                        dag_id=dag_id, session=session
                    )
                )
                if not sd_latest_version_and_updated_datetime:
                    self.log.warning("Serialized DAG %s no longer exists", dag_id)
                    del self.dags[dag_id]
                    del self.dags_last_fetched[dag_id]
                    del self.dags_hash[dag_id]
                    return None

                sd_latest_version, sd_last_updated_datetime = sd_latest_version_and_updated_datetime

                if (
                    sd_last_updated_datetime > self.dags_last_fetched[dag_id]
                    or sd_latest_version != self.dags_hash[dag_id]
                ):
                    self._add_dag_from_db(dag_id=dag_id, session=session)

            return self.dags.get(dag_id)

        # If asking for a known subdag, we want to refresh the parent
        dag = None
        root_dag_id = dag_id
        if dag_id in self.dags:
            dag = self.dags[dag_id]

        # If DAG Model is absent, we can't check last_expired property. Is the DAG not yet synchronized?
        orm_dag = DagModel.get_current(root_dag_id, session=session)
        if not orm_dag:
            return self.dags.get(dag_id)

        # If the dag corresponding to root_dag_id is absent or expired
        is_missing = root_dag_id not in self.dags
        is_expired = (
            orm_dag.last_expired and dag and dag.last_loaded and dag.last_loaded < orm_dag.last_expired
        )
        if is_expired:
            # Remove associated dags so we can re-add them.
            self.dags = {key: dag for key, dag in self.dags.items()}
        if is_missing or is_expired:
            # Reprocess source file.
            found_dags = self.process_file(
                filepath=correct_maybe_zipped(orm_dag.fileloc), only_if_updated=False
            )

            # If the source file no longer exports `dag_id`, delete it from self.dags
            if found_dags and dag_id in [found_dag.dag_id for found_dag in found_dags]:
                return self.dags[dag_id]
            elif dag_id in self.dags:
                del self.dags[dag_id]
        return self.dags.get(dag_id)

    def _add_dag_from_db(self, dag_id: str, session: Session):
        """Add DAG to DagBag from DB."""
        from airflow.models.serialized_dag import SerializedDagModel

        row: SerializedDagModel | None = SerializedDagModel.get(dag_id, session)
        if not row:
            return None

        row.load_op_links = self.load_op_links
        dag = row.dag
        self.dags[dag.dag_id] = dag
        self.dags_last_fetched[dag.dag_id] = timezone.utcnow()
        self.dags_hash[dag.dag_id] = row.dag_hash

    def process_file(self, filepath, only_if_updated=True, safe_mode=True):
        """Given a path to a python module or zip file, import the module and look for dag objects within."""
        from airflow.sdk.definitions._internal.contextmanager import DagContext

        # if the source file no longer exists in the DB or in the filesystem,
        # return an empty list
        # todo: raise exception?

        if filepath is None or not os.path.isfile(filepath):
            return []

        try:
            # This failed before in what may have been a git sync
            # race condition
            file_last_changed_on_disk = datetime.fromtimestamp(os.path.getmtime(filepath))
            if (
                only_if_updated
                and filepath in self.file_last_changed
                and file_last_changed_on_disk == self.file_last_changed[filepath]
            ):
                return []
        except Exception as e:
            self.log.exception(e)
            return []

        # Ensure we don't pick up anything else we didn't mean to
        DagContext.autoregistered_dags.clear()

        self.captured_warnings.pop(filepath, None)
        with capture_with_reraise() as captured_warnings:
            if filepath.endswith(".py") or not zipfile.is_zipfile(filepath):
                mods = self._load_modules_from_file(filepath, safe_mode)
            else:
                mods = self._load_modules_from_zip(filepath, safe_mode)

        if captured_warnings:
            formatted_warnings = []
            for msg in captured_warnings:
                category = msg.category.__name__
                if (module := msg.category.__module__) != "builtins":
                    category = f"{module}.{category}"
                formatted_warnings.append(f"{msg.filename}:{msg.lineno}: {category}: {msg.message}")
            self.captured_warnings[filepath] = tuple(formatted_warnings)

        found_dags = self._process_modules(filepath, mods, file_last_changed_on_disk)

        self.file_last_changed[filepath] = file_last_changed_on_disk
        return found_dags

    @property
    def dag_warnings(self) -> set[DagWarning]:
        """Get the set of DagWarnings for the bagged dags."""
        from airflow.models.dagwarning import DagWarning, DagWarningType

        # None means this feature is not enabled. Empty set means we don't know about any pools at all!
        if self.known_pools is None:
            return set()

        def get_pools(dag) -> dict[str, set[str]]:
            return {dag.dag_id: {task.pool for task in dag.tasks}}

        pool_dict: dict[str, set[str]] = {}
        for dag in self.dags.values():
            pool_dict.update(get_pools(dag))

        warnings: set[DagWarning] = set()
        for dag_id, dag_pools in pool_dict.items():
            nonexistent_pools = dag_pools - self.known_pools
            if nonexistent_pools:
                warnings.add(
                    DagWarning(
                        dag_id,
                        DagWarningType.NONEXISTENT_POOL,
                        f"Dag '{dag_id}' references non-existent pools: {sorted(nonexistent_pools)!r}",
                    )
                )
        return warnings

    def _load_modules_from_file(self, filepath, safe_mode):
        from airflow.sdk.definitions._internal.contextmanager import DagContext

        if not might_contain_dag(filepath, safe_mode):
            # Don't want to spam user with skip messages
            if not self.has_logged:
                self.has_logged = True
                self.log.info("File %s assumed to contain no DAGs. Skipping.", filepath)
            return []

        self.log.debug("Importing %s", filepath)
        mod_name = get_unique_dag_module_name(filepath)

        if mod_name in sys.modules:
            del sys.modules[mod_name]

        DagContext.current_autoregister_module_name = mod_name

        def parse(mod_name, filepath):
            try:
                loader = importlib.machinery.SourceFileLoader(mod_name, filepath)
                spec = importlib.util.spec_from_loader(mod_name, loader)
                new_module = importlib.util.module_from_spec(spec)
                sys.modules[spec.name] = new_module
                loader.exec_module(new_module)
                return [new_module]
            except KeyboardInterrupt:
                # re-raise ctrl-c
                raise
            except BaseException as e:
                # Normally you shouldn't catch BaseException, but in this case we want to, as, pytest.skip
                # raises an exception which does not inherit from Exception, and we want to catch that here.
                # This would also catch `exit()` in a dag file
                DagContext.autoregistered_dags.clear()
                self.log.exception("Failed to import: %s", filepath)
                if self.dagbag_import_error_tracebacks:
                    self.import_errors[filepath] = traceback.format_exc(
                        limit=-self.dagbag_import_error_traceback_depth
                    )
                else:
                    self.import_errors[filepath] = str(e)
                return []

        dagbag_import_timeout = settings.get_dagbag_import_timeout(filepath)

        if not isinstance(dagbag_import_timeout, (int, float)):
            raise TypeError(
                f"Value ({dagbag_import_timeout}) from get_dagbag_import_timeout must be int or float"
            )

        if dagbag_import_timeout <= 0:  # no parsing timeout
            return parse(mod_name, filepath)

        timeout_msg = (
            f"DagBag import timeout for {filepath} after {dagbag_import_timeout}s.\n"
            "Please take a look at these docs to improve your DAG import time:\n"
            f"* {get_docs_url('best-practices.html#top-level-python-code')}\n"
            f"* {get_docs_url('best-practices.html#reducing-dag-complexity')}"
        )
        with timeout(dagbag_import_timeout, error_message=timeout_msg):
            return parse(mod_name, filepath)

    def _load_modules_from_zip(self, filepath, safe_mode):
        from airflow.sdk.definitions._internal.contextmanager import DagContext

        mods = []
        with zipfile.ZipFile(filepath) as current_zip_file:
            for zip_info in current_zip_file.infolist():
                zip_path = Path(zip_info.filename)
                if zip_path.suffix not in [".py", ".pyc"] or len(zip_path.parts) > 1:
                    continue

                if zip_path.stem == "__init__":
                    self.log.warning("Found %s at root of %s", zip_path.name, filepath)

                self.log.debug("Reading %s from %s", zip_info.filename, filepath)

                if not might_contain_dag(zip_info.filename, safe_mode, current_zip_file):
                    # todo: create ignore list
                    # Don't want to spam user with skip messages
                    if not self.has_logged:
                        self.has_logged = True
                        self.log.info(
                            "File %s:%s assumed to contain no DAGs. Skipping.", filepath, zip_info.filename
                        )
                    continue

                mod_name = zip_path.stem
                if mod_name in sys.modules:
                    del sys.modules[mod_name]

                DagContext.current_autoregister_module_name = mod_name
                try:
                    sys.path.insert(0, filepath)
                    current_module = importlib.import_module(mod_name)
                    mods.append(current_module)
                except Exception as e:
                    DagContext.autoregistered_dags.clear()
                    fileloc = os.path.join(filepath, zip_info.filename)
                    self.log.exception("Failed to import: %s", fileloc)
                    if self.dagbag_import_error_tracebacks:
                        self.import_errors[fileloc] = traceback.format_exc(
                            limit=-self.dagbag_import_error_traceback_depth
                        )
                    else:
                        self.import_errors[fileloc] = str(e)
                finally:
                    if sys.path[0] == filepath:
                        del sys.path[0]
        return mods

    def _process_modules(self, filepath, mods, file_last_changed_on_disk):
        from airflow.models.dag import DAG  # Avoid circular import
        from airflow.sdk.definitions._internal.contextmanager import DagContext

        top_level_dags = {(o, m) for m in mods for o in m.__dict__.values() if isinstance(o, DAG)}

        top_level_dags.update(DagContext.autoregistered_dags)

        DagContext.current_autoregister_module_name = None
        DagContext.autoregistered_dags.clear()

        found_dags = []

        for dag, mod in top_level_dags:
            dag.fileloc = mod.__file__
            try:
                dag.validate()
                self.bag_dag(dag=dag)
            except AirflowClusterPolicySkipDag:
                pass
            except Exception as e:
                self.log.exception("Failed to bag_dag: %s", dag.fileloc)
                self.import_errors[dag.fileloc] = f"{type(e).__name__}: {e}"
                self.file_last_changed[dag.fileloc] = file_last_changed_on_disk
            else:
                found_dags.append(dag)
        return found_dags

    def bag_dag(self, dag: DAG):
        """
        Add the DAG into the bag.

        :raises: AirflowDagCycleException if a cycle is detected in this dag or its subdags.
        :raises: AirflowDagDuplicatedIdException if this dag or its subdags already exists in the bag.
        """
        check_cycle(dag)  # throws if a task cycle is found

        dag.resolve_template_files()
        dag.last_loaded = timezone.utcnow()

        try:
            # Check policies
            settings.dag_policy(dag)

            for task in dag.tasks:
                # The listeners are not supported when ending a task via a trigger on asynchronous operators.
                if getattr(task, "end_from_trigger", False) and get_listener_manager().has_listeners:
                    raise AirflowException(
                        "Listeners are not supported with end_from_trigger=True for deferrable operators. "
                        "Task %s in DAG %s has end_from_trigger=True with listeners from plugins. "
                        "Set end_from_trigger=False to use listeners.",
                        task.task_id,
                        dag.dag_id,
                    )

                settings.task_policy(task)
        except (AirflowClusterPolicyViolation, AirflowClusterPolicySkipDag):
            raise
        except Exception as e:
            self.log.exception(e)
            raise AirflowClusterPolicyError(e)

        try:
            prev_dag = self.dags.get(dag.dag_id)
            if prev_dag and prev_dag.fileloc != dag.fileloc:
                raise AirflowDagDuplicatedIdException(
                    dag_id=dag.dag_id,
                    incoming=dag.fileloc,
                    existing=self.dags[dag.dag_id].fileloc,
                )
            self.dags[dag.dag_id] = dag
            self.log.debug("Loaded DAG %s", dag)
        except (AirflowDagCycleException, AirflowDagDuplicatedIdException):
            # There was an error in bagging the dag. Remove it from the list of dags
            self.log.exception("Exception bagging dag: %s", dag.dag_id)
            raise

    def collect_dags(
        self,
        dag_folder: str | Path | None = None,
        only_if_updated: bool = True,
        include_examples: bool = conf.getboolean("core", "LOAD_EXAMPLES"),
        safe_mode: bool = conf.getboolean("core", "DAG_DISCOVERY_SAFE_MODE"),
    ):
        """
        Look for python modules in a given path, import them, and add them to the dagbag collection.

        Note that if a ``.airflowignore`` file is found while processing
        the directory, it will behave much like a ``.gitignore``,
        ignoring files that match any of the patterns specified
        in the file.

        **Note**: The patterns in ``.airflowignore`` are interpreted as either
        un-anchored regexes or gitignore-like glob expressions, depending on
        the ``DAG_IGNORE_FILE_SYNTAX`` configuration parameter.
        """
        if self.read_dags_from_db:
            return

        self.log.info("Filling up the DagBag from %s", dag_folder)
        dag_folder = dag_folder or self.dag_folder
        # Used to store stats around DagBag processing
        stats = []

        # Ensure dag_folder is a str -- it may have been a pathlib.Path
        dag_folder = correct_maybe_zipped(str(dag_folder))

        files_to_parse = list_py_file_paths(dag_folder, safe_mode=safe_mode)

        if include_examples:
            from airflow import example_dags

            example_dag_folder = next(iter(example_dags.__path__))

            files_to_parse.extend(list_py_file_paths(example_dag_folder, safe_mode=safe_mode))

        for filepath in files_to_parse:
            try:
                file_parse_start_dttm = timezone.utcnow()
                found_dags = self.process_file(filepath, only_if_updated=only_if_updated, safe_mode=safe_mode)

                file_parse_end_dttm = timezone.utcnow()
                stats.append(
                    FileLoadStat(
                        file=filepath.replace(settings.DAGS_FOLDER, ""),
                        duration=file_parse_end_dttm - file_parse_start_dttm,
                        dag_num=len(found_dags),
                        task_num=sum(len(dag.tasks) for dag in found_dags),
                        dags=str([dag.dag_id for dag in found_dags]),
                        warning_num=len(self.captured_warnings.get(filepath, [])),
                    )
                )
            except Exception as e:
                self.log.exception(e)

        self.dagbag_stats = sorted(stats, key=lambda x: x.duration, reverse=True)

    def collect_dags_from_db(self):
        """Collect DAGs from database."""
        from airflow.models.serialized_dag import SerializedDagModel

        with Stats.timer("collect_db_dags"):
            self.log.info("Filling up the DagBag from database")

            # The dagbag contains all rows in serialized_dag table. Deleted DAGs are deleted
            # from the table by the scheduler job.
            self.dags = SerializedDagModel.read_all_dags()

    def dagbag_report(self):
        """Print a report around DagBag loading stats."""
        stats = self.dagbag_stats
        dag_folder = self.dag_folder
        duration = sum((o.duration for o in stats), timedelta()).total_seconds()
        dag_num = sum(o.dag_num for o in stats)
        task_num = sum(o.task_num for o in stats)
        table = tabulate(stats, headers="keys")

        report = textwrap.dedent(
            f"""\n
        -------------------------------------------------------------------
        DagBag loading stats for {dag_folder}
        -------------------------------------------------------------------
        Number of DAGs: {dag_num}
        Total task number: {task_num}
        DagBag parsing time: {duration}\n{table}
        """
        )
        return report

    @provide_session
    def sync_to_db(self, bundle_name: str, bundle_version: str | None, session: Session = NEW_SESSION):
        """Save attributes about list of DAG to the DB."""
        from airflow.dag_processing.collection import update_dag_parsing_results_in_db

        update_dag_parsing_results_in_db(
            bundle_name,
            bundle_version,
            self.dags.values(),  # type: ignore[arg-type]  # We should create a proto for DAG|LazySerializedDAG
            self.import_errors,
            self.dag_warnings,
            session=session,
        )


def generate_md5_hash(context):
    fileloc = context.get_current_parameters()["fileloc"]
    return hashlib.md5(fileloc.encode()).hexdigest()


class DagPriorityParsingRequest(Base):
    """Model to store the dag parsing requests that will be prioritized when parsing files."""

    __tablename__ = "dag_priority_parsing_request"

    # Adding a unique constraint to fileloc results in the creation of an index and we have a limitation
    # on the size of the string we can use in the index for MySQL DB. We also have to keep the fileloc
    # size consistent with other tables. This is a workaround to enforce the unique constraint.
    id = Column(String(32), primary_key=True, default=generate_md5_hash, onupdate=generate_md5_hash)

    # The location of the file containing the DAG object
    # Note: Do not depend on fileloc pointing to a file; in the case of a
    # packaged DAG, it will point to the subpath of the DAG within the
    # associated zip.
    fileloc = Column(String(2000), nullable=False)

    def __init__(self, fileloc: str) -> None:
        super().__init__()
        self.fileloc = fileloc

    def __repr__(self) -> str:
        return f"<DagPriorityParsingRequest: fileloc={self.fileloc}>"
